Few-Shot Open-Set Audio Classification Using Attention Information-Fused Prototypes
Most existing audio classification methods suppose that each query (testing) sample belongs to a class of support (training) samples, and misrecognize samples of unseen classes as seen classes (cannot reject samples of unseen classes). In this study, we propose a method for Few-shot Open-set Audio Classification (FOAC), which can recognize query samples of seen classes after updating the model using a few support samples, and meanwhile reject query samples from unseen classes. We design a model consisting of an encoder and a classifier. The encoder is the backbone of a ResNet used for extracting embeddings. The classifier consists of prototype generators of few-shot classes and open-set c...
| Authors | Yanxiong Li, Jiaxin Tan, Qianqian Li et al. |
|---|---|
| Primary category | eess.AS |
| Published | 1 July 2026 |
| Last updated at source | 1 July 2026 |
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